bert-base-uncased-QnA-MLQA_Dataset

This model is a fine-tuned version of bert-base-uncased.

Model description

For more information on how it was created, check out the following link: https://github.com/DunnBC22/NLP_Projects/blob/main/Question%26Answer/ML%20QA/ML_QA_Question%26Answer_with_BERT.ipynb

Intended uses & limitations

This model is intended to demonstrate my ability to solve a complex problem using technology.

Training and evaluation data

Dataset Source: https://huggingface.co./datasets/mlqa/viewer/mlqa.en.en/test

Histogram of Input (Both Context & Question) Lengths

Histogram of Context Lengths

Histogram of Question Lengths

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

• learning_rate: 2e-05
• train_batch_size: 8
• eval_batch_size: 8
• seed: 42
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• num_epochs: 3

Training results

Metric Name	Metric Value
Exact Match	59.6146
F1	73.3002

• All values in the above chart are rounded to the nearest ten-thousandth.

Framework versions

• Transformers 4.31.0
• Pytorch 2.0.1+cu118
• Datasets 2.14.2
• Tokenizers 0.13.3